In a day and age where e-commerce become increasing popular, many product venders devote a large amount of resources on developing and using on-line sales platforms that present images of products in a listing, and facilitate sales by providing product descriptions, online reviews, and information videos on individual product pages. Many of the on-line sales platforms also provide product search functions that identify subsets of all available products based on search keywords entered by a user. Although online sales platforms also provide an avenue for sales of home appliances, pure online sales platforms cannot meet users' desire to try out home appliances, to learn about their many features, to touch and manipulate the home appliances in person, or to see the home appliance operating in a physical environment that mimics the intended operating environment of the users' homes. Pure online sale platforms also cannot provide a user with any real-time, personalized attention and assistance at a location where the users have the in-person close-proximity experiences with the appliances.
Brick and mortar stores are becoming increasingly rare and costly to operate. Good and efficient sales staff are not only difficult to find but also expensive to maintain. In addition, one sales person may develop a good rapport and win a sale with one customer, but may face avoidance and rejection with respect to a different customer. In the short amount of time that a customer typically spends inside a brick and mortar store, a real human sales person simply does not have the personality, energy, capacity, or knowledge to provide truly personalized service and assistance to each customer that walks through the door.
Recently, there has been a great deal of interest in developing in-store sales robots. However, many of the state-of-the-art in-store sales robots that have been contemplated are crude combinations of the existing e-commerce backend system with added natural language processing capabilities. The sales robots simply mimic a crude natural language exchange with a customer based on generic language models and keyword identifications, and are not much more effective than the existing online sales platforms. Generic statistical modeling of mass customer data on the backend system also does not take into account of an individual customer's unique in-store experience with the products, and does not take into account the uniqueness of appliance shopping in comparison with shopping of other types of products or merchandise. The advancement of in-store sales robots are also severely limited by the availability of relevant data and the lack of efficient and effective ways of selecting relevant parameters and prioritizing data processing and storage tasks.
Therefore, an improved method and system of providing personalized, on-location information exchange regarding home appliances that is fast, efficient, and effective is needed.